Clinical characteristics, outcomes, and risk factors for mortality in hospitalized patients with COVID-19 and cancer history: a propensity score-matched study

Abstract

Background COVID-19 has caused great concern for patients with underlying medical conditions. We aimed to determine the prognosis of patients with current or previous cancer with either a PCR-confirmed COVID-19 infection or a probable diagnosis according to chest CT scan. Methods We conducted a case control study in a referral hospital on confirmed COVID-19 adult patients with and without a history of cancer from February25th to April21st, 2020. Patients were matched according to age, gender, and underlying diseases including ischemic heart disease (IHD), diabetes mellitus (DM), and hypertension (HTN). Demographic features, clinical data, comorbidities, symptoms, vital signs, laboratory findings, and chest computed tomography (CT) images have been extracted from patients’ medical records. Multivariable logistic regression was used to estimate odd ratios and 95% confidence intervals of each factor of interest with outcomes. Results Fifty-three confirmed COVID-19 patients with history of cancer were recruited and compared with 106 non-cancerous COVID-19 patients as controls. Male to female ratio was 1.33 and 45% were older than 65. Dyspnea and fever were the most common presenting symptoms in our population with 57.86 and 52.83% respectively. Moreover, dyspnea was significantly associated with an increased rate of mortality in the cancer subgroup (p = 0.013). Twenty-six patients (49%) survived among the cancer group while 89 patients (84%) survived in control (p = 0.000). in cancer group, patients with hematologic cancer had 63% mortality while patients with solid tumors had 37%. multivariate analysis model for survival prediction showed that history of cancer, impaired consciousness level, tachypnea, tachycardia, leukocytosis and thrombocytopenia were associated with an increased risk of death. Conclusion In our study, cancer increased the mortality rate and hospital stay of COVID-19 patients and this effect remains significant after adjustment of confounders. Compared to solid tumors, hematologic malignancies have been associated with worse consequences and higher mortality rate. Clinical and para-clinical indicators were not appropriate to predict death in these patients.

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

“…Gender, age, weight, height, BMI, past medical history of comorbidities (i.e. diabetes, hypertension, ischemic heart disease, and cancer), history of smoking, initial vital signs, including respiratory rate (RR), O 2 saturation (O2Sat), pulse rate (PR), systolic blood pressure (SBP), diastolic blood pressure (DBP), temperature in degrees Celsius (T) and the level of consciousness were obtained and recorded [ 6 , [41] , [42] , [43] ].…”

“…These included aspartate aminotransferase in U/L (AST), alanine aminotransferase (ALT) in U/L, alkaline phosphatase (ALP) in U/L, total and direct bilirubin (T.Bill and D.Bill, respectively) in mg/dL, hemoglobin (HB) in g/dL, white blood cells (WBC) in/mm 3 , venous blood gas analysis of acidity (PH), carbon dioxide concentration (PCO2), and bicarbonate concentration (HCO3), C-reactive protein in mg/L (CRP), platelet count in/mm 3 (Plt), blood creatinine level in mg/dL (Cr), blood urea nitrogen (BUN) in mg/dL, prothrombin time (PT) in seconds, partial thromboplastin time (PTT) in seconds, prothrombin time normalized with the international normalized ratio (INR), procalcitonin levels (PCT) in ng/dL, and sodium and potassium (Na and K, respectively) in mEq/L. We also used the differential counts of neutrophils, lymphocytes, monocytes, and eosinophils in percentages (Neutr.Diff, Lymph.Diff, Mono.Diff, and Eosin.Diff, respectively) [ 6 , [41] , [42] , [43] ].…”

“…To obtain radiological data, we designed a questionnaire layout (presented in Supplemental material) based on which the following information was gathered: (a) type of parenchymal abnormality, such as (i) ground-glass opacities (GGO) (ii) consolidation, (iii) reticular pattern, and (iv) mixed pattern; (b) axial and craniocaudal distribution; (c) pleural effusion; (d) pericardial effusion; and (e) emphysema [ 6 , [41] , [42] , [43] ]. In addition, we adapted the 6-zone segmentation, which includes upper, middle and lower zone of each lung [ 6 , [41] , [42] , [43] ]. Both left and right lung were divided into three zones including carina upper level, between the carina and inferior pulmonary vein, and below the inferior pulmonary vein level [ 6 , [41] , [42] , [43] ].…”

“…In addition, we adapted the 6-zone segmentation, which includes upper, middle and lower zone of each lung [ 6 , [41] , [42] , [43] ]. Both left and right lung were divided into three zones including carina upper level, between the carina and inferior pulmonary vein, and below the inferior pulmonary vein level [ 6 , [41] , [42] , [43] ]. Then, we separately evaluated each zone involvement and scored between 0 and 4 (0: no involvement, 1: 1%-25% involved, 2: 26%-50% involved, 3: 51%-75% involved, and 4: 76%-100% involved) [ 6 , [41] , [42] , [43] , [44] , [45] , [46] ].…”

“…The total involvement score (Inv.Score) was calculated by summing up different zones scores. All radiological scores were assigned by consensus of two experienced radiologists to report CT scan findings and a third senior radiologist settled any discordance/dispute between the two [ 6 , [41] , [42] , [43] ].…”