Objective To characterise the symptoms of coronavirus disease 2019 (covid-19). Design Population based cohort study. Setting Iceland. Participants All individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcription polymerase chain reaction (RT-PCR) between 17 March and 30 April 2020. Cases were identified by three testing strategies: targeted testing guided by clinical suspicion, open invitation population screening based on self referral, and random population screening. All identified cases were enrolled in a telehealth monitoring service, and symptoms were systematically monitored from diagnosis to recovery. Main outcome measures Occurrence of one or more of 19 predefined symptoms during follow-up. Results Among 1564 people positive for SARS-CoV-2, the most common presenting symptoms were myalgia (55%), headache (51%), and non-productive cough (49%). At the time of diagnosis, 83 (5.3%) individuals reported no symptoms, of whom 49 (59%) remained asymptomatic during follow-up. At diagnosis, 216 (14%) and 349 (22%) people did not meet the case definition of the Centers for Disease Control and Prevention and the World Health Organization, respectively. Most (67%) of the SARS-CoV-2-positive patients had mild symptoms throughout the course of their disease. Conclusion In the setting of broad access to RT-PCR testing, most SARS-CoV-2-positive people were found to have mild symptoms. Fever and dyspnoea were less common than previously reported. A substantial proportion of SARS-CoV-2-positive people did not meet recommended case definitions at the time of diagnosis.

Background The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 in unvaccinated adults at the time of diagnosis. Methods All SARS-CoV-2-positive adults in Iceland were prospectively enrolled into a telehealth service at diagnosis. A multivariable proportional-odds logistic regression model was derived from information obtained during the enrollment interview of those diagnosed between February 27 and December 31, 2020 who met the inclusion criteria. Outcomes were defined on an ordinal scale: (1) no need for escalation of care during follow-up; (2) need for urgent care visit; (3) hospitalization; and (4) admission to intensive care unit (ICU) or death. Missing data were multiply imputed using chained equations and the model was internally validated using bootstrapping techniques. Decision curve analysis was performed. Results The prognostic model was derived from 4756 SARS-CoV-2-positive persons. In total, 375 (7.9%) only required urgent care visits, 188 (4.0%) were hospitalized and 50 (1.1%) were either admitted to ICU or died due to complications of COVID-19. The model included age, sex, body mass index (BMI), current smoking, underlying conditions, and symptoms and clinical severity score at enrollment. On internal validation, the optimism-corrected Nagelkerke’s R2 was 23.4% (95%CI, 22.7–24.2), the C-statistic was 0.793 (95%CI, 0.789-0.797) and the calibration slope was 0.97 (95%CI, 0.96–0.98). Outcome-specific indices were for urgent care visit or worse (calibration intercept -0.04 [95%CI, -0.06 to -0.02], Emax 0.014 [95%CI, 0.008–0.020]), hospitalization or worse (calibration intercept -0.06 [95%CI, -0.12 to -0.03], Emax 0.018 [95%CI, 0.010–0.027]), and ICU admission or death (calibration intercept -0.10 [95%CI, -0.15 to -0.04] and Emax 0.027 [95%CI, 0.013–0.041]). Conclusion Our prognostic model can accurately predict the later need for urgent outpatient evaluation, hospitalization, and ICU admission and death among unvaccinated SARS-CoV-2-positive adults in the general population at the time of diagnosis, using information obtained by telephone interview.

Background: The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 at the time of diagnosis and determine risk factors for severe disease. Methods: All SARS-CoV-2-positive adults in Iceland were prospectively enrolled into a telehealth service at diagnosis. A multivariable proportional-odds logistic regression model was derived from information obtained during the enrollment interview with those diagnosed before May 1, 2020 and validated in those diagnosed between May 1 and December 31, 2020. Outcomes were defined on an ordinal scale; no need for escalation of care during follow-up, need for outpatient visit, hospitalization, and admission to intensive care unit (ICU) or death. Risk factors were summarized as odds ratios (OR) adjusted for confounders identified by a directed acyclic graph. Results: The prognostic model was derived from and validated in 1,625 and 3,131 individuals, respectively. In total, 375 (7.9%) only required outpatient visits, 188 (4.0%) were hospitalized and 50 (1.1%) were either admitted to ICU or died due to complications of COVID-19. The model included age, sex, body mass index (BMI), current smoking, underlying conditions, and symptoms and clinical severity score at enrollment. Discrimination and calibration were excellent for outpatient visit or worse (C-statistic 0.75, calibration intercept 0.04 and slope 0.93) and hospitalization or worse (C-statistic 0.81, calibration intercept 0.16 and slope 1.03). Age was the strongest risk factor for adverse outcomes with OR of 75- compared to 45- year-olds, ranging from 5.29-17.3. Higher BMI consistently increased the risk and chronic obstructive pulmonary disease and chronic kidney disease correlated with worse outcomes. Conclusion: Our prognostic model can accurately predict the outcome of SARS-CoV-2 infection using information that is available at the time of diagnosis.

Background and Objectives Revised Icelandic guidelines proposed a restrictive haemoglobin (Hb) threshold of 70 g/l for red blood cell (RBC) transfusions in general, but 100 g/l for malignancies/bone marrow suppression. Chronic lymphocytic leukaemia (CLL) is frequently complicated by anaemia. The objective was to investigate RBC transfusion practices in CLL. Materials and methods This retrospective nation‐wide study utilized an Icelandic registry of CLL patients diagnosed between 2003 and 2016. Medical records were reviewed and haemoglobin transfusion triggers compared for two periods: Earlier (2003–2012) and latter (2013–2017). Results Two hundred and thirteen patients were diagnosed with CLL over the period whereof 77 (36·2%) received RBC transfusion(s). Median time from diagnosis to first transfusion was 2·2 years. Higher age, Rai stage 3/4 at diagnosis (P < 0·05) and chemotherapy (P < 0·001) were associated with increased odds of transfusions. Shorter time to first transfusion correlated with higher age (P < 0·001) and Rai stage (P = 0·02) at diagnosis. The mean Hb trigger was 90·4 and 81·2 in the earlier and latter period respectively (P = 0·01). This difference in Hb triggers was most pronounced in patients without documented bone marrow involvement, or 80·5 g/l compared to 93·5 g/l (P = 0·004). The median time from diagnosis to transfusion was longer in the latter period (2·9 years vs. 1·6 years, P = 0·01). After RBC transfusions the survival decreased significantly (P < 0·001). Conclusion One‐third of CLL patients received RBC transfusions but few were heavily transfused. Older age, Rai stage, and chemotherapy predicted RBC use. The Hb transfusion trigger decreased over time while time to first RBC transfusion increased. RBC transfusions predict poor survival.

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